{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Stock's Data Feature Importance (Classification)"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-28T18:21:42.617Z",
          "iopub.execute_input": "2020-06-28T18:21:42.626Z",
          "shell.execute_reply": "2020-06-28T18:21:43.564Z",
          "iopub.status.idle": "2020-06-28T18:21:43.545Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2020-01-01'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": [
              "            Adj Close  Close  High   Low  Open    Volume\n",
              "Date                                                    \n",
              "2014-01-02       3.95   3.95  3.98  3.84  3.85  20548400\n",
              "2014-01-03       4.00   4.00  4.00  3.88  3.98  22887200\n",
              "2014-01-06       4.13   4.13  4.18  3.99  4.01  42398300\n",
              "2014-01-07       4.18   4.18  4.25  4.11  4.19  42932100\n",
              "2014-01-08       4.18   4.18  4.26  4.14  4.23  30678700"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.85</td>\n",
              "      <td>20548400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>3.98</td>\n",
              "      <td>22887200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.01</td>\n",
              "      <td>42398300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.19</td>\n",
              "      <td>42932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.23</td>\n",
              "      <td>30678700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-28T18:21:43.551Z",
          "iopub.execute_input": "2020-06-28T18:21:43.556Z",
          "iopub.status.idle": "2020-06-28T18:21:44.764Z",
          "shell.execute_reply": "2020-06-28T18:21:44.789Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,0)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,0)\n",
        "dataset['Returns'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-28T18:21:44.771Z",
          "iopub.execute_input": "2020-06-28T18:21:44.776Z",
          "iopub.status.idle": "2020-06-28T18:21:44.801Z",
          "shell.execute_reply": "2020-06-28T18:21:44.795Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": [
              "            Adj Close  Close  High   Low  Open    Volume  Increase_Decrease  \\\n",
              "Date                                                                          \n",
              "2014-01-03       4.00   4.00  4.00  3.88  3.98  22887200                  1   \n",
              "2014-01-06       4.13   4.13  4.18  3.99  4.01  42398300                  1   \n",
              "2014-01-07       4.18   4.18  4.25  4.11  4.19  42932100                  0   \n",
              "2014-01-08       4.18   4.18  4.26  4.14  4.23  30678700                  0   \n",
              "2014-01-09       4.09   4.09  4.23  4.05  4.20  30667600                  0   \n",
              "\n",
              "            Buy_Sell_on_Open  Buy_Sell   Returns  \n",
              "Date                                              \n",
              "2014-01-03                 1         1  0.012658  \n",
              "2014-01-06                 1         1  0.032500  \n",
              "2014-01-07                 1         0  0.012106  \n",
              "2014-01-08                 0         0  0.000000  \n",
              "2014-01-09                 0         1 -0.021531  "
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Increase_Decrease</th>\n",
              "      <th>Buy_Sell_on_Open</th>\n",
              "      <th>Buy_Sell</th>\n",
              "      <th>Returns</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>3.98</td>\n",
              "      <td>22887200</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.012658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.01</td>\n",
              "      <td>42398300</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.032500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.19</td>\n",
              "      <td>42932100</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.012106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.23</td>\n",
              "      <td>30678700</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-09</th>\n",
              "      <td>4.09</td>\n",
              "      <td>4.09</td>\n",
              "      <td>4.23</td>\n",
              "      <td>4.05</td>\n",
              "      <td>4.20</td>\n",
              "      <td>30667600</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.021531</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-28T18:21:44.809Z",
          "iopub.execute_input": "2020-06-28T18:21:44.814Z",
          "iopub.status.idle": "2020-06-28T18:21:44.830Z",
          "shell.execute_reply": "2020-06-28T18:21:45.522Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": [
              "(1509, 10)"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-28T18:21:44.843Z",
          "iopub.execute_input": "2020-06-28T18:21:44.847Z",
          "iopub.status.idle": "2020-06-28T18:21:44.855Z",
          "shell.execute_reply": "2020-06-28T18:21:45.524Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset[['Open', 'High', 'Low','Adj Close', 'Volume', 'Buy_Sell_on_Open', 'Buy_Sell', 'Returns']]\n",
        "y = dataset['Increase_Decrease']"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-28T18:21:44.861Z",
          "iopub.execute_input": "2020-06-28T18:21:44.865Z",
          "iopub.status.idle": "2020-06-28T18:21:44.873Z",
          "shell.execute_reply": "2020-06-28T18:21:45.526Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Create decision tree classifer object\n",
        "clf = RandomForestClassifier(random_state=0, n_jobs=-1)\n",
        "\n",
        "# Train model\n",
        "model = clf.fit(X, y)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-28T18:21:44.881Z",
          "iopub.execute_input": "2020-06-28T18:21:44.888Z",
          "iopub.status.idle": "2020-06-28T18:21:45.504Z",
          "shell.execute_reply": "2020-06-28T18:21:45.529Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate feature importances\n",
        "importances = model.feature_importances_"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-28T18:21:45.510Z",
          "iopub.execute_input": "2020-06-28T18:21:45.514Z",
          "iopub.status.idle": "2020-06-28T18:21:45.611Z",
          "shell.execute_reply": "2020-06-28T18:21:45.617Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort feature importances in descending order\n",
        "indices = np.argsort(importances)[::-1]\n",
        "\n",
        "# Rearrange feature names so they match the sorted feature importances\n",
        "names = [dataset.columns[i] for i in indices]\n",
        "\n",
        "# Create plot\n",
        "plt.figure()\n",
        "\n",
        "# Create plot title\n",
        "plt.title(\"Feature Importance\")\n",
        "\n",
        "# Add bars\n",
        "plt.bar(range(X.shape[1]), importances[indices])\n",
        "\n",
        "# Add feature names as x-axis labels\n",
        "plt.xticks(range(X.shape[1]), names, rotation=90)\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              "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\n"
            ],
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2020-06-28T18:21:45.637Z",
          "iopub.execute_input": "2020-06-28T18:21:45.641Z",
          "iopub.status.idle": "2020-06-28T18:21:45.792Z",
          "shell.execute_reply": "2020-06-28T18:21:45.797Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "file_extension": ".py",
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "version": "3.5.5",
      "name": "python",
      "mimetype": "text/x-python"
    },
    "kernelspec": {
      "argv": [
        "C:\\Users\\Tin Hang\\Anaconda3\\envs\\py35\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.23.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}